Beyond the Basics: Data Visualization in Python

Data visualization is essential for anyone working with data, but sometimes it can be difficult to create impactful visualizations in Python. In this workshop, we will move beyond the plotting basics and explore how to make compelling static, animated, and interactive visualizations.


The human brain excels at finding patterns in visual representations, which is why data visualizations are essential to any analysis. Done right, they bridge the gap between those analyzing the data and those consuming the analysis. However, learning to create impactful, aesthetically-pleasing visualizations can often be challenging. This session will equip you with the skills to make customized visualizations for your data using Python.

While there are many plotting libraries to choose from, the prolific Matplotlib library is always a great place to start. Since various Python data science libraries utilize Matplotlib under the hood, familiarity with Matplotlib itself gives you the flexibility to fine tune the resulting visualizations (e.g., add annotations, animate, etc.). This session will also introduce interactive visualizations using HoloViz, which provides a higher-level plotting API capable of using Matplotlib and Bokeh (a Python library for generating interactive, JavaScript-powered visualizations) under the hood.

Section 1: Getting Started With Matplotlib

We will begin by familiarizing ourselves with Matplotlib. Moving beyond the default options, we will explore how to customize various aspects of our visualizations. By the end of this section, you will be able to generate plots using the Matplotlib API directly, as well as customize the plots that libraries like pandas and Seaborn create for you.

Section 2: Moving Beyond Static Visualizations

Static visualizations are limited in how much information they can show. To move beyond these limitations, we can create animated and/or interactive visualizations. Animations make it possible for our visualizations to tell a story through movement of the plot components (e.g., bars, points, lines). Interactivity makes it possible to explore the data visually by hiding and displaying information based on user interest. In this section, we will focus on creating animated visualizations using Matplotlib before moving on to create interactive visualizations in the next section.

Section 3: Building Interactive Visualizations for Data Exploration

When exploring our data, interactive visualizations can provide the most value. Without having to create multiple iterations of the same plot, we can use mouse actions (e.g., click, hover, zoom, etc.) to explore different aspects and subsets of the data. In this section, we will learn how to use a few of the libraries in the HoloViz ecosystem to create interactive visualizations for exploring our data utilizing the Bokeh backend.

Stefanie Molin
Audience Level
240 minutes
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Speaker name:
Stefanie Molin
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